@inproceedings{kahardipraja-etal-2020-exploring,
title = "Exploring Span Representations in Neural Coreference Resolution",
author = "Kahardipraja, Patrick and
Vyshnevska, Olena and
Lo{\'a}iciga, Sharid",
editor = "Braud, Chlo{\'e} and
Hardmeier, Christian and
Li, Junyi Jessy and
Louis, Annie and
Strube, Michael",
booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.codi-1.4",
doi = "10.18653/v1/2020.codi-1.4",
pages = "32--41",
abstract = "In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.",
}
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<abstract>In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.</abstract>
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%0 Conference Proceedings
%T Exploring Span Representations in Neural Coreference Resolution
%A Kahardipraja, Patrick
%A Vyshnevska, Olena
%A Loáiciga, Sharid
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Louis, Annie
%Y Strube, Michael
%S Proceedings of the First Workshop on Computational Approaches to Discourse
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kahardipraja-etal-2020-exploring
%X In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.
%R 10.18653/v1/2020.codi-1.4
%U https://aclanthology.org/2020.codi-1.4
%U https://doi.org/10.18653/v1/2020.codi-1.4
%P 32-41
Markdown (Informal)
[Exploring Span Representations in Neural Coreference Resolution](https://aclanthology.org/2020.codi-1.4) (Kahardipraja et al., CODI 2020)
ACL